scholarly journals Agent-Based Modeling of Urban Growth With Communications Inspired by Particle Swarm Optimization Algoritm

2017 ◽  
Vol 5 (1) ◽  
pp. 49-64
Author(s):  
Farnaz Kaviari ◽  
Mohamad Sadi Mesgari ◽  
Farhad Hosseinali ◽  
Samane Vaezi ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Urban Growth ◽  
Particle Swarm ◽  
Agent Based Modeling ◽  
Swarm Optimization ◽  
Agent Based

Modeling dynamic urban growth using cellular automata and particle swarm optimization rules

2011 ◽  
Vol 102 (3) ◽  
pp. 188-196 ◽  
Author(s):  
Yongjiu Feng ◽  
Yan Liu ◽  
Xiaohua Tong ◽  
Miaolong Liu ◽  
Susu Deng
Keyword(s):  
Particle Swarm Optimization ◽  
Cellular Automata ◽  
Urban Growth ◽  
Particle Swarm ◽  
Swarm Optimization

Comparison of Multi Agent Based PSO Approach for Optimal Power Flows with Security Constraints

2011 ◽  
Vol 110-116 ◽  
pp. 5206-5210
Author(s):  
K.Ravi Kumar ◽  
S. Anand ◽  
M. Sydulu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Square Lattice ◽  
Two Dimensional ◽  
Swarm Optimization ◽  
Agent Based ◽  
Cubic Lattice ◽  
Optimal Power ◽  
Fitness Value ◽  
Multi Agent

This paper proposes a comparison of new evolutionary multi agent based particle swarm optimization algorithms for solving optimal power flows with security constraints (line flows and bus voltages). These methods combine the multi agents in two dimensional and cubic lattice structures with particle swarm optimization (PSO) to form two new algorithms. In both Two Dimensional Lattice Structured Multi Agent based Particle Swarm Optimization (TDLSMAPSO) and Cubic Lattice Structured Multi Agent based Particle Swarm Optimization (CLSMAPSO), an agent represents a particle in cubic lattice structure to PSO, and a candidate solution to the OPF problem. All agents live in a square and cubic lattice like environments, with agents fixed on a lattice point in the ascending order of their fitness value. In order to obtain the optimal solution, each agent in cubic and square lattice competes and cooperates with its neighbors. Making use of these agent-agent interactions, CLSMAPSO and TDLSMAPSO realizes the purpose of minimizing the objective function value. CLSMAPSO and TDLSMAPSO realizations were applied to IEEE 30 bus system. Simulation results show that proposed approaches gives better solution than earlier reported approaches in quick time.

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